Enhancing AM part printability at the early design stage: a numerical and data-driven approach of DfAM

研究的总体目标是从设计阶段提高增材制造（AM）零件的可制造性。虽然增材制造设计（DfAM）的概念在过去十年中越来越受欢迎，但设计原则通常被视为经验法则并手动实施。它导致在应用程序中的计算设计和优化过程（例如拓扑优化，格生成等）的困难。此外，现有的AM仿真和优化方法通常会遇到大规模或复杂零件的计算时间过长的问题。本研究采用数据驱动的方法来解决这一问题（例如机器学习（ML））：1）识别并纳入DfAM原则（从实验数据中归纳）到数值设计优化过程中; 2）在概念设计阶段实现对大型零件快速准确的可制造性仿真，允许零件在设计周期的早期针对AM进行调整。实现该项目目标的一项主要努力是将机器学习方法从传统的“黑匣子”性质中转移出来。“基于物理的机器学习”（PBML）概念的应用被认为是实现这一目标的可行手段。受PBML在空气动力学领域的成功启发，我们提出在AM过程中嵌入控制方程（例如，用于捕获打印期间的热机械行为的热耗散方程），同时用实验数据训练以实现对打印结果的快速但物理上准确的预测，从而允许相应地进行后续的设计修改/优化。物理控制方程的嵌入可以通过所选ML技术（例如深度神经网络）和/或定制ML模型架构的损失函数的定制公式来实现。除了开发物理信息机器学习模型外，另一个重点是将训练的ML模型与传统的数值方法相结合，以利用这两种技术的优势进行多尺度，高保真和快速仿真以及设计优化。ML方法在中尺度到宏观尺度上具有上级速度，因为计算时间与域大小解耦，但根据所使用的训练数据，它们可能无法在微观尺度上产生准确的结果。在需要更多细节的局部区域选择性地应用数值方法可以有效地解决这个问题，而不会产生太多额外的计算工作，因为它只在微观尺度上运行。研究的结果可以通过增加“第一次正确”的可能性，从而减少与失败的打印和/或试错相关的浪费，从而使任何与AM相关的应用受益。此外，能够在自动化数值设计过程中推广和实施DfAM规则，减少了对经验的依赖，以确保成功的AM，鼓励采用AM技术和分散的自动化制造。它将使那些拥有偏远场地但需要复杂、高精度部件的行业（例如海上风电场）受益匪浅。随着DfAM原则的鼓励/实施，将设计出利用AM制造自由度的更高效的部件。它可以在产品的整个生命周期内进一步减少对环境的影响（例如车辆部件的相关排放）。

The overall aim of the research is to improve the manufacturability of additively manufactured (AM) part from the design stage. While the concept of Design for Additive Manufacturing (DfAM) has gained popularity over the past decade, the design principles are usually treated as empirical rules-of-thumbs and implemented manually. It results in difficulties in application to computational design and optimisation processes (e.g. topology optimisation, lattice generation etc.). Furthermore, existing simulation and optimisation methods for AM often suffer from the formidably long computing time for large-scale or complex parts. This research tackles the problem by employing data-driven method (e.g. machine learning (ML)) to: 1) identify and incorporate the DfAM principles (as generalised from experimental data) into the numerical design optimisation processes; 2) achieve quick and accurate manufacturability simulation for large-scale parts at the conceptual design stage, allowing the parts to be adapted for AM early in the design cycle.One main endeavour for realising the goals of the project is shifting the machine learning methods away from the traditional 'black-box' nature. The application of the 'physics-based machine learning' (PBML) concept is identified as a viable means for the objective. Inspired by the success of PBML in the field of aerodynamics, we propose embedding the governing equations during the AM process (e.g. heat dissipation equations to capture the thermomechanical behaviour during printing) in the machine learning model at the same time of training with experimental data to achieve fast yet physically accurate predictions of the outcome of printing, allowing subsequent design modifications/optimisations to be conducted accordingly. The embedding of physical governing equations can be achieved through bespoke formulation of the loss function of the chosen ML techniques (e.g. deep neural network) and/or customised ML model architecture. In addition to the development of physic-informed machine learning models, another focus is the integration of trained ML models with traditional numerical methods to leverage the strength of both techniques for multi-scale, high-fidelity, and rapid simulation as well as design optimisation. ML methods have superior speed at meso- to macro-scale since the computing time is de-coupled from the domain size, but depending on the training data used, they might be unable to produce accurate results at the microscale. Selectively applying numerical methods at localised regions where greater details are needed can effectively resolve the issue without incurring too much extra computation effort as it is only run at the microscale. The outcome of the research can benefit any AM-related applications by increasing the likelihood of 'first-time-right' and hence reducing the waste associated with failed prints and/or trial-and-error. Moreover, being able to generalise and implement DfAM rules in automated numerical design process reduces the reliance on experience to ensure successful AM, encouraging the adoption of AM technology and decentralised, automated manufacturing. It will significantly benefit industries that have remote sites but require complex, high precision parts (e.g. offshore wind farms). With DfAM principles being encouraged/enforced, more efficient parts that leverage the manufacturing freedom of AM will be designed. It can lead to further reduction of environmental impact (e.g associated emission of vehicle parts) over the product's whole lifecycle.